Predicting the risk of drug–drug interactions in psychiatric hospitals: a retrospective longitudinal pharmacovigilance study

OBJECTIVES: The aim was to use routine data available at a patient’s admission to the hospital to predict polypharmacy and drug–drug interactions (DDI) and to evaluate the prediction performance with regard to its usefulness to support the efficient management of benefits and risks of drug prescriptions. DESIGN: Retrospective, longitudinal study. SETTING: We used data from a large multicentred pharmacovigilance project carried out in eight psychiatric hospitals in Hesse, Germany. PARTICIPANTS: Inpatient episodes consecutively discharged between 1 October 2017 and 30 September 2018 (year 1) or 1 January 2019 and 31 December 2019 (year 2). OUTCOME MEASURES: The proportion of rightly classified hospital episodes. METHODS: We used gradient boosting to predict respective outcomes. We tested the performance of our final models in unseen patients from another calendar year and separated the study sites used for training from the study sites used for performance testing. RESULTS: A total of 53 909 episodes were included in the study. The models’ performance, as measured by the area under the receiver operating characteristic, was ‘excellent’ (0.83) and ‘acceptable’ (0.72) compared with common benchmarks for the prediction of polypharmacy and DDI, respectively. Both models were substantially better than a naive prediction based solely on basic diagnostic grouping. CONCLUSION: This study has shown that polypharmacy and DDI can be predicted from routine data at patient admission. These predictions could support an efficient management of benefits and risks of hospital prescriptions, for instance by including pharmaceutical supervision early after admission for patients at risk before pharmacological treatment is established.